Visual Sentiment Prediction with Deep Convolutional Neural Networks

Abstract

Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propose a novel visual sentiment prediction framework that performs image understanding with Deep Convolutional Neural Networks (CNN). Specifically, the proposed sentiment prediction framework performs transfer learning from a CNN with millions of parameters, which is pre-trained on large-scale data for object recognition. Experiments conducted on two real-world datasets from Twitter and Tumblr demonstrate the effectiveness of the proposed visual sentiment analysis framework.

References in corpus (2)

• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• What is Tumblr: A Statistical Overview and Comparison

Cited by in corpus (7)

• Survey on Visual Sentiment Analysis
• Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion
• Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
• Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
• Affective Image Content Analysis: Two Decades Review and New Perspectives
• Visual Themes and Sentiment on Social Networks To Aid First Responders During Crisis Events
• Computational Emotion Analysis From Images: Recent Advances and Future Directions